Volume 18, Issue 2 (4-2018)                   Modares Mechanical Engineering 2018, 18(2): 159-169 | Back to browse issues page

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Jahanbakhshi A, Ahmadi Nadooshan A. Simulation of passive heating solar wall and prediction the temperature by Artificial Neural Networks and Adaptive Neuro-Fuzzy model (ANFIS). Modares Mechanical Engineering 2018; 18 (2) :159-169
URL: http://mme.modares.ac.ir/article-15-8473-en.html
1- Department of Mechanical Engineering, Shahrekord University, Shahrekord, Iran
2- Mechanical Engineering Department, Engineering Faculty, Shahrekord University, shahrekord
Abstract:   (4208 Views)
In this paper, the interior air of the room heated by the solar wall (Trombe) with respect to Heat conduction in the wall is numerically simulated. Momentum and energy equations have been Algebraic with finite volume method and at the same time are solved with SIMPLE algorithm. First, a reference model is introduced and the results are presented and then with this reference model, the effective parameters on the performance of the wall were investigated and ultimately the most optimal geometry for the solar wall with the best performance was voted.As well, rectangular fins has been put on the surface of the absorbent wall, in order to increase its efficiency. The results show that solar wall with rectangular fins in all air gaps has better performance than plain wall and for example, with rectangular fins in the air gap equal to 1 m, room temperature is approximately 1.24% more than the simple Trombe wall. Then, using Artificial Neural Networks and ANFIS the values increase of room temperature by increasing the number of fins has been projected on the wall. The neural network was trained in such a way that the average temperature of the room depends on the number of fins on the surface of the absorbent the solar wall. The results compare mean squared error and root-mean-square error showed that ANFIS With the mean squared error equal to 0.742599 has good performance and acceptable accuracy compared with Neural Network With the mean squared error equal 1.1 to predict temperature.
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Article Type: Research Article | Subject: Solar Energy & Radiation
Received: 2017/11/30 | Accepted: 2018/01/9 | Published: 2018/01/25

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